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Category: internet – Page 128

Elon Musk Suggests That a Brain Parasite Is Forcing Humans to Create Superhuman AI

Elon Musk tweeted a fascinating — and frankly unsettling — theory last night about how a brain parasite might be forcing all humans to create advanced AI.

The Tesla CEO was responding to a story from National Geographic about how toxoplasmosis, a common parasite often found in cats, seems to be causing hyenas to be reckless around predators such as lions. In a staggering and perhaps facetious leap of logic, Musk suggested that the parasite is actually what’s causing humans to create advanced artificial intelligence.

“Toxoplasmosis infects rats, then cats, then humans who make cat videos,” Musk tweeted on Friday. “AI trains achieves superhuman intelligence training on Internet cat videos, thus making toxoplasmosis the true arbiter of our destiny.”

No knowledge, only intuition!

Article originally published on LINKtoLEADERS under the Portuguese title “Sem saber ler nem escrever!”

In the 80s, “with no knowledge, only intuition”, I discovered the world of computing. I believed computers could do everything, as if it were an electronic God. But when I asked the TIMEX Sinclair 1000 to draw the planet Saturn — I am fascinated by this planet, maybe because it has rings —, I only glimpse a strange message on the black and white TV:

0/0

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AI system makes models like DALL-E 2 more creative

Researchers develop a new method that uses multiple models to create more complex images with better understanding.

The internet had a collective feel-good moment with the introduction of DALL-E, an artificial intelligence-based image generator inspired by artist Salvador Dali and the lovable robot WALL-E that uses natural language to produce whatever mysterious and beautiful image your heart desires. Seeing typed-out inputs like “smiling gopher holding an ice cream cone” instantly spring to life clearly resonated with the world.

Getting said smiling gopher and attributes to pop up on your screen is not a small task.

A new AI system from MIT makes models like DALL-E 2 more creative by using multiple models to create more complex images with better understanding.

How explainable artificial intelligence can propel the growth of industry 4.0

The very first industrial revolution historically kicked off with the introduction of steam-and water-powered technology. We have come a long way since then, with the current fourth industrial revolution, or Industry 4.0, being focused on utilizing new technology to boost industrial efficiency.

Some of these technologies include the internet of things (IoT), cloud computing, cyber-physical systems, and artificial intelligence (AI). AI is the key driver of Industry 4.0, automating to self-monitor, interpret, diagnose, and analyze all by themselves. AI methods, such as machine learning (ML), (DL), processing (NLP), and computer vision (CV), help industries forecast their maintenance needs and cut down on downtime.

However, to ensure the smooth, stable deployment and integration of AI-based systems, the actions and results of these systems must be made comprehensible, or, in other words, “explainable” to experts. In this regard, explainable AI (XAI) focuses on developing algorithms that produce human-understandable results made by AI-based systems. Thus, XAI deployment is useful in Industry 4.0.

NASA’s Revamped Eyes on the Solar System Lets You Explore Space in Your Browser

NASA released an impressive desktop app some years back called “NASA’s Eyes Visualization,” which allowed you to check out the solar system, along with all the spacecraft exploring it. But who installs programs anymore? It graduated to the web recently, and now it has an updated interface and tools. Simply head to the “Eyes on the Solar System” site on your device of choice, and start exploring.

The main interface of the new site is simply the orbits of the planets, color-coded with highlights to show you their current positions. The layout is accurate for the current time, but you can use either buttons or the slider at the bottom to speed up or reverse time. It goes as high or as low as three years per second. You have to figure this revamp was supposed to coincide with NASA’s Artemis program, but that’s taking a bit longer than expected to get off the ground.

In addition to the clickable overview of the solar system, there are several suggested “points of interest” on the side of the screen. These are all along the same lines as the Eyes on the Solar System engine, but some (like the Perseverance landing simulation) load on a separate page.

Startup Behind AI Image Generator Stable Diffusion Is In Talks To Raise At A Valuation Up To $1 Billion

With the image generator Stable Diffusion, you can conjure within seconds a potrait of Beyoncé as if painted by Vincent van Gogh, a cyberpunk cityscape in the style of 18th century Japanese artist Hokusai and a complex alien world straight out of science fiction. Released to the public just two weeks ago, it’s become one of several popular AI-powered text-to-image generators, including DALL-E 2, that have taken the internet by storm.

Now, the company behind Stable Diffusion is in discussions to raise $100 million from investors, according to three people with knowledge of the matter.

Stability AI’s open source text-to-image generator was released to the general public in late August. It has already accumulated massive community goodwill — and controversy over how it’s been used by individuals on websites like 4chan.

Advanced Metamaterials

A look at revolutionary new materials with seemingly impossible properties.
Start protecting your internet experience today with 77% off a 3 year plan by using code ‘ISAAC’ at http://www.NordVPN.com/ISAAC
Metamaterials offer many properties normally not found in nature, from superior lenses and communications to stealth applications, potentially offering invisibility. Today we’ll examine the science behind that and look at many other possible applications.

AMA thread tonight (Thursday March 29) at 6 PM EST over at /r/space on reddit: https://www.reddit.com/r/space/comments/881rbl/ama_this_is_i…_anything/

SFIA Merchandise available: https://www.signil.com/sfia.
Visit our Website: http://www.isaacarthur.net.
Join the Facebook Group: https://www.facebook.com/groups/1583992725237264/
Support the Channel on Patreon: https://www.patreon.com/IsaacArthur.
Visit the sub-reddit: https://www.reddit.com/r/IsaacArthur/
Listen or Download the audio of this episode from Soundcloud: https://soundcloud.com/isaac-arthur-148927746/advanced-metamaterials.
Cover Art by Jakub Grygier: https://www.artstation.com/artist/jakub_grygier.

Script Editors.
Derek Hightower.
Keith Blockus.
Luke Parrish.
Mitch Armstrong.

Graphics Team:
Edward Nardella.
Jarred Eagley.
Justin Dixon.
Katie Byrne.
Kris Holland of Mafic Stufios: www.maficstudios.com.
Misho Yordanov.
Pierre Demet.
Sergio Botero: https://www.artstation.com/sboterod?fref=gc.
Stefan Blandin.

Music Supervisor.

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Researchers develop new strategies to teach computers to learn like humans do

As demonstrated by breakthroughs in various fields of artificial intelligence (AI), such as image processing, smart health care, self-driving vehicles and smart cities, this is undoubtedly the golden period of deep learning. In the next decade or so, AI and computing systems will eventually be equipped with the ability to learn and think the way humans do—to process continuous flow of information and interact with the real world.

However, current AI models suffer from a performance loss when they are trained consecutively on new information. This is because every time new data is generated, it is written on top of existing data, thus erasing previous information. This effect is known as “catastrophic forgetting.” A difficulty arises from the stability-plasticity issue, where the AI model needs to update its memory to continuously adjust to the new information, and at the same time, maintain the stability of its current knowledge. This problem prevents state-of-the-art AI from continually learning from real world information.

Edge computing systems allow computing to be moved from the cloud storage and to near the , such as devices connected to the Internet of Things (IoTs). Applying continual learning efficiently on resource limited edge computing systems remains a challenge, although many continual learning models have been proposed to solve this problem. Traditional models require high computing power and large memory capacity.

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